273 research outputs found
Learning to Incorporate Texture Saliency Adaptive Attention to Image Cartoonization
Image cartoonization is recently dominated by generative adversarial networks
(GANs) from the perspective of unsupervised image-to-image translation, in
which an inherent challenge is to precisely capture and sufficiently transfer
characteristic cartoon styles (e.g., clear edges, smooth color shading,
abstract fine structures, etc.). Existing advanced models try to enhance
cartoonization effect by learning to promote edges adversarially, introducing
style transfer loss, or learning to align style from multiple representation
space. This paper demonstrates that more distinct and vivid cartoonization
effect could be easily achieved with only basic adversarial loss. Observing
that cartoon style is more evident in cartoon-texture-salient local image
regions, we build a region-level adversarial learning branch in parallel with
the normal image-level one, which constrains adversarial learning on
cartoon-texture-salient local patches for better perceiving and transferring
cartoon texture features. To this end, a novel cartoon-texture-saliency-sampler
(CTSS) module is proposed to dynamically sample cartoon-texture-salient patches
from training data. With extensive experiments, we demonstrate that texture
saliency adaptive attention in adversarial learning, as a missing ingredient of
related methods in image cartoonization, is of significant importance in
facilitating and enhancing image cartoon stylization, especially for
high-resolution input pictures.Comment: Proceedings of the 39th International Conference on Machine Learning,
PMLR 162:7183-7207, 202
The Value of Alternative Data in Credit Risk Prediction: Evidence from a Large Field Experiment
Recently, the high penetration of mobile devices and internet access offers a new source of fine-grained user behavior data (aka âalternative dataâ) to improve the financial credit risk assessment. This paper conducts a comprehensive evaluation of the value of alternative data on microloan platforms with a large field experiment. Our machine-learning-based empirical analyses demonstrate that alternative data can significantly improve the prediction accuracy of borrowersâ default behavior and increase platform profits. Cellphone usage and mobility trace information perform the best among the multiple sources of alternative data. Moreover, we find that our proposed framework helps financial institutions extend their service to more lower-income and less-educated loan applicants from less-developed geographical areas â those historically disadvantaged population who have been largely neglected in the past. Our study demonstrates the tremendous potential of leveraging alternative data to alleviate such inequality in the financial service markets, while in the meantime achieving higher platform revenues
Modeling Mg II h, k and Triplet Lines at Solar Flare Ribbons
Observations from the \textit{Interface Region Imaging Spectrograph}
(\textsl{IRIS}) often reveal significantly broadened and non-reversed profiles
of the Mg II h, k and triplet lines at flare ribbons. To understand the
formation of these optically thick Mg II lines, we perform plane parallel
radiative hydrodynamics modeling with the RADYN code, and then recalculate the
Mg II line profiles from RADYN atmosphere snapshots using the radiative
transfer code RH. We find that the current RH code significantly underestimates
the Mg II h \& k Stark widths. By implementing semi-classical perturbation
approximation results of quadratic Stark broadening from the STARK-B database
in the RH code, the Stark broadenings are found to be one order of magnitude
larger than those calculated from the current RH code. However, the improved
Stark widths are still too small, and another factor of 30 has to be multiplied
to reproduce the significantly broadened lines and adjacent continuum seen in
observations. Non-thermal electrons, magnetic fields, three-dimensional effects
or electron density effect may account for this factor. Without modifying the
RADYN atmosphere, we have also reproduced non-reversed Mg II h \& k profiles,
which appear when the electron beam energy flux is decreasing. These profiles
are formed at an electron density of
and a temperature of K, where the source function slightly
deviates from the Planck function. Our investigation also demonstrates that at
flare ribbons the triplet lines are formed in the upper chromosphere, close to
the formation heights of the h \& k lines
Inferring Economic Condition Uncertainty from Electricity Big Data
Inferring the uncertainties in economic conditions are of significant
importance for both decision makers as well as market players. In this paper,
we propose a novel method based on Hidden Markov Model (HMM) to construct the
Economic Condition Uncertainty (ECU) index that can be used to infer the
economic condition uncertainties. The ECU index is a dimensionless index ranges
between zero and one, this makes it to be comparable among sectors, regions and
periods. We use the daily electricity consumption data of nearly 20 thousand
firms in Shanghai from 2018 to 2020 to construct the ECU indexes. Results show
that all ECU indexes, no matter at sectoral level or regional level,
successfully captured the negative impacts of COVID-19 on Shanghai's economic
conditions. Besides, the ECU indexes also presented the heterogeneities in
different districts as well as in different sectors. This reflects the facts
that changes in uncertainties of economic conditions are mainly related to
regional economic structures and targeted regulation policies faced by sectors.
The ECU index can also be easily extended to measure uncertainties of economic
conditions in different fields which has great potentials in the future
Striking a Balance: Harnessing Both the Business and Informational Value of Online Reviews through Resource-matching
A majority of consumers now are getting used to consulting reviews before making any purchase decisions. Although we have witnessed fruitful studies in this stream of literature, there lacks sufficient knowledge regarding whether and how we can realize the information and business values simultaneously. We undertook to bridge this gap. Drawn from the cognitive tuning theory and resource-matching theory, we posit that review sentiment would intertwine with the information richness of a review to affect consumersâ judgment of review helpfulness and purchase decision. Our empirical results demonstrate that the information richness of a review, overall, moderates the U-shaped relationship between review sentiment and review helpfulness, as well as the inverted U-shaped relationship between review sentiment and consumer purchase likelihood. These findings unravel certain conditions under which increasing both purchases and review helpfulness could be achieved, which, therefore, offer non-trivial insights into business practice about review-featuring designs
Brake or Step On the Gas? Empirical Analyses of Credit Effects on Individual Consumption
Understanding the effects of credit on consumption is crucial for guiding usersâ consumption behavior, designing financial marketing strategies, and identifying credit\u27s value in stimulating the economy. Whereas several studies have endeavored on this issue, most simply utilize observations of a single credit channel and/or focus on an overall effect without considering the potentially heterogeneous short-term and long-term consumption changes. This study, leveraging a quasi-experimental design with high-resolution transaction data, examines how people respond to credit in both short- and long-term periods. Results show that credit usersâ consumption amount significantly expand by 51.74% after getting access to credit in the short term. However, they ultimately cut their consumption by 4.02% to cope with financial constraints in the long term. We also reveal and quantify the spillover effects of credit on consumption with savings channels. We draw on regulatory focus theory to rationalize the changes on consumersâ consumption behavior after credit activation
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